In control theory Advanced Process Control (APC) is a broad term composed of different kinds of process control tools, often used for solving multivariable control problems or discrete control problem. APC is composed of different kinds of process control tools, for example Model Predictive Control (MPC), Statistical Process Control (SPC), Run2Run (R2R), Fault Detection and Classification (FDC), Sensor control and Feedback systems. APC applications are often used for solving multivariable control or discrete control problems. In some instances an APC system is connected to a Distributed Control System (DCS). The APC application will calculate moves that are sent to regulatory controllers. Historically the interfaces between DCS and APC systems were dedicated software interfaces. Alternatively, the communication protocol between these systems is managed via the industry standard Object Linking and Embedding (OLE) for Process Control (OPC) protocol.
APC can be found in the (petro) chemical industries where it makes it possible to control multivariable control problems. Since these controllers contain the dynamic relationships between variables it can predict in the future how variables will behave. Based on these predictions, actions can be taken now to maintain variables within their limits. APC is used when the models can be estimated and do not vary too much. In the complex semiconductor industry where several hundred steps with multiple re-entrant possibilities occur, APC plays an important role for control the overall production. In addition, APC is more and more used in other industries. In the mining industry for example, successful applications of APC (often combined with Fuzzy Logic) have been successfully implemented. APC implementation is in more than 95% of cases done as a replacement of an old control such as a proportional-integral-derivative (HD) controllers.
APC performance, for example the performance of a MPC system, is significantly dependent on the quality of the target system model. Therefore, a very important part of APC design is system model identification by performing experiments on the system. It is well-known fact that these experiments represent the highest costs of APC implementation. The reason is that the experiments are traditionally done by step testing, requiring long duration, where normal system operation and production processes are significantly disrupted.
Step testing is typically performed by opening the control loop (leaving the process control on the manual control of an operator), waiting for the system to reach a steady state, making a step change in selected input and observing the behavior for eventual manual intervention preventing process limits crossing. This is repeated for individual inputs. The long duration of this type of testing is caused by the need to wait for steady state before step changes can be applied. The disruptions are caused by opening the control loop and applying step changes to the inputs.